Methods and apparatus for machine learning predictions of manufacture processes

ABSTRACT

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/395,940 filed on Apr. 26, 2019, which is a continuation of U.S.patent application Ser. No. 16/046,519, filed on Jul. 26, 2018, andissued as U.S. Pat. No. 10,274,933, which is a continuation of, andclaims priority under 35 U.S.C. § 120 o U.S. patent application Ser. No.15/340,338 filed Nov. 1, 2016, and issued as U.S. Pat. No. 10,281,902,and entitled “METHODS AND APPARATUS FOR MACHINE LEARNING PREDICTIONS OFMANUFACTURE PROCESSES”, the disclosures of which are incorporated hereinby reference in their entireties.

TECHNICAL FIELD

The present disclosure relates generally to manufacture processes, andmore particularly to the processing of digital models for manufactureprocesses through machine learning and artificial intelligencetechniques.

BACKGROUND

Often, product manufacturers receive customer orders specified inelectronic files including Computer Assisted Design (CAD) models. A needexists for methods and apparatus to support the prediction of appraisalsand production alternatives related to manufactured products, includingtimes, manufacture methods, and costs incurred in manufacture processes.Many manufacture companies rely on human experience alone or have nostandardized methodologies to predict appraisals and other aspectsassociated with the manufacture of products. Relying on human experiencealone can result in inaccurate and can lead to inconsistencies acrossappraisals and other predictions made by different human estimators withrespect to similar or identical products. Moreover, the lack of reliableobjective manufacture appraisal and prediction tools capable to operatein near real-time can significantly contribute to the overhead ofmanufacture of products.

Thus, a need exists for methods and apparatus for discretization andreliable manufacture analysis tools to accurately predict aspectsassociated with the production of products specified in CAD or otherdigital-based models.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a predictive system for manufactureprocesses operatively coupled to a client compute device, according toan embodiment.

FIG. 2 is a schematic block diagram illustrating a predictive storagedevice, a mesh analysis engine, a point cloud analysis engine, asymbolic function engine, a predictive engine, memories, and a networkcommunication interface of a predictive system for manufactureprocesses, according to an embodiment.

FIG. 3 is a flowchart illustrating a method to generate a set ofpredictions associated with manufacture processes of a physical object,according to an embodiment.

FIG. 4 is a flowchart illustrating a method to generate a set ofpredictions associated with manufacture processes of a physical object,according to another embodiment.

FIG. 5 is a schematic block diagram illustrating components of apredictive engine in a predictive system for manufacture processes,according to an embodiment.

FIG. 6 is a flowchart illustrating a method to train machine learningmodels of a predictive system for manufacture processes, according to anembodiment.

FIG. 7 is a flowchart illustrating an example of a method to implementan evolutionary model of a predictive system for manufacture processes,according to an embodiment.

FIG. 8 is a data structure for symbolic regression built via anevolutionary model of a predictive system for manufacture processes,according to an embodiment.

FIG. 9 is a flowchart illustrating a method to build a random forestclassifier machine learning model, according to an embodiment.

FIG. 10 is a flowchart illustrating a method for applying a splittingcriterion to a random forest classifier machine learning model,according to an embodiment.

FIG. 11 is a cross-entity flowchart illustrating a method to computepredictions in a predictive system for manufacture processes, accordingto an embodiment.

FIG. 12 is an example of a signal flow illustrating a method to computepredictions associated with manufacture of physical objects based onknowledge acquisition from a random forest classifier, an extremelyrandomized trees regressor, and a logistic regression classifier,according to an embodiment.

FIG. 13 is an example of an output from a graphical user interface ofthe predictive system for manufacture processes and related to predictedoutcomes associated with the manufacture of a physical object, accordingto an embodiment.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology may bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a thorough understandingof the subject technology. It, however, will be clear and apparent thatthe subject technology is not limited to the specific details set forthherein and may be practiced without these specific details. In someinstances, well-known structures and components are shown in blockdiagram form to avoid obscuring the concepts of the subject technology.

The terms “computer”, “processor”, “computer processor”, “computedevice” or the like should be expansively construed to cover any kind ofelectronic device with data processing capabilities including, by way ofnon-limiting example, a digital signal processor (DSP), amicrocontroller, a field programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), or any other electronic computingdevice comprising one or more processors of any kind, or any combinationthereof.

As used herein, the phrase “for example,” “such as”, “for instance” andvariants thereof describe non-limiting embodiments of the presentlydisclosed subject matter. Reference in the specification to “one case”,“some cases”, “other cases” or variants thereof means that a particularfeature, structure or characteristic described in connection with theembodiment(s) is included in at least one embodiment of the presentlydisclosed subject matter. Thus the appearance of the phrase “one case”,“some cases”, “other cases” or variants thereof does not necessarilyrefer to the same embodiment(s).

It is appreciated that, unless specifically stated otherwise, certainfeatures of the presently disclosed subject matter, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the presently disclosed subject matter, which are, forbrevity, described in the context of a single embodiment, may also beprovided separately or in any suitable sub-combination.

The subject technology is related to methods and apparatus fordiscretization and manufacturability analysis of computer assisteddesign models. In one embodiment, the subject technology implements acomputer-based method for the reception of an electronic file with adigital model representative of a physical object, for example, amechanical part or other suitable object. The computer-based methodincludes a discretization process of the digital model to determine afirst set of parameters associated with physical attributes of thephysical object. The first set of parameters includes any of a volume ofthe physical object, a data value corresponding to a surface area of thephysical object, a 3-tuple data structure with a length value, a widthvalue, and a height value associated with a prism enclosing the digitalmodel, a 3-tuple data structure with coordinate data values of a centerof the prism, a data value corresponding to a volume of a convex hullenclosing the digital model, a data value corresponding to a number ofholes associated with the physical object (i.e., genus), and/or othersuitable parameters described herein.

The computer-based method further includes a determination of a secondset of parameters associated with physical attributes of the physicalobject. The determination is based on three-dimensional coordinatesassociated with the physical object. The second set of parametersincludes any of a data value corresponding to a measure of the physicalobject calculated based on orientation of at least one surface of thephysical object, any of a data value corresponding to a measure of thephysical object calculated based on symmetric attributes associated withthe physical object, and any of a data value corresponding to a measureof the physical object calculated based on distances between surfaces ofthe physical object.

The computer-based method infers, based on the second set of parametersand at least on one machine learning model, a set of axioms associatedwith a manufacture process of the physical object. Such a set of axiomsdefine characteristics of the physical object related to specificmanufacture processes and/or techniques. A set of predicted outcomesassociated with an overall manufacture process of the physical object isthen determined based on the set of axioms. In another embodiment, thesubject technology implements a computer-based method for the receptionof an electronic file with a digital model representative of a physicalobject, for example, a mechanical part or other suitable object. Thecomputer-based method includes a discretization process of the digitalmodel to determine a first set of parameters associated with physicalattributes of the physical object. The first set of parameters includesany of a data value corresponding to a measure of the physical objectcalculated based on, dimensional analysis of the discretized digitalmodel, orientation of at least one surface of the physical object, anyof a data value corresponding to a measure of the physical objectcalculated based on symmetric attributes associated with the physicalobject, and any of a data value corresponding to a measure of thephysical object calculated based on distances between surfaces of thephysical object.

The computer-based method further includes a determination of a secondset of parameters associated with physical attributes of the physicalobject. The determination is based on the first set of parameters and aset of symbolic functions derived from an evolutionary computationmodel. The second set of parameters corresponds to physical attributesof the physical object. The second set of parameters includes any of adata value corresponding to a measure of the physical object calculatedbased on orientation of at least one surface of the physical object, anyof a data value corresponding to a measure of the physical objectcalculated based on symmetric attributes associated with the physicalobject, and any of a data value corresponding to a measure of thephysical object calculated based on distances between surfaces of thephysical object.

The computer-based method infers based on the second set of parameters,and at least on one machine learning model, a set of axioms associatedwith a manufacture process of the physical object. Such a set of axiomsdefine characteristics of the physical object related specificmanufacture processes and/or techniques. A set of predicted outcomesassociated with an overall manufacture process of the physical object isthen determined based on the set of axioms.

Advantageously, the subject technology provides manufacture predictionsin near real-time. Specifically, in some instances, the time delayintroduced by the compute device implementing the computer-based methodsand network transmissions described herein is on a millisecond orderbetween 150 ms and 1000 ms. Thus, the responsiveness of thecomputer-based method is immediate and perceived as a part of amechanical action (e.g., a request submitted via a user interface) madeby a user.

The subject technology provides objective, accurate, and consistentclassifications and/or predictions regarding manufacturing process,including estimated times, optimal costs, comparisons of fabricationmaterials, and other suitable information. The classifications and/orpredictions are reliable; that is, assessments for the manufacture ofsimilar products result in similar or equivalent outcomes. The subjecttechnology operates in near real-time and thus, optimizes manufacturingprocess by decreasing overhead associated with human-based estimations.

FIG. 1 is a schematic diagram of a predictive system for manufactureprocesses operatively coupled to a client compute device, according toan embodiment. Such a Predictive System for Manufacture Processes(hereinafter PSMP) 109 can provide predictions or estimations in nearreal-time regarding manufacturing processes associated with a physicalobject, for example, a mechanical part, an ornament, an electronicdevice, or other suitable physical object conveyed in an electronic filesuch as a CAD file, a scanned copy of a model drafted by hand, and/orother electronic files graphically describing a model of a physicalobject. In some instances, a local user (not shown in FIG. 1) canconnect directly to the PSMP system 109, in other instances a remoteuser 101 can connect to the PSMP system via a network, for example,network 103.

Network 103 can include one or more types of communication networks. Forexample communication networks can include, the Internet, a local areanetwork (LAN), a wide area network (WAN), a metropolitan area network(MAN), various types of telephone networks (including, for example,Public Switch Telephone Network (PSTN) with Digital Subscriber Line(DSL) technology) or mobile networks (including for example GlobalSystem Mobile (GSM) communication, General Packet Radio Service (GPRS),Code Division Multiple Access (CDMA), and other suitable mobile networktechnologies), or any combination thereof. Communication within thenetwork can be realized through any suitable connection (including wiredor wireless) and communication technology or standard (wireless fidelity(WiFi®), 4G, long-term evolution (LTE™), or other suitable standard).

Compute device 105 can be configured with one or more computerprocessors and a computer memory (including transitory computer memoryand/or non-transitory computer memory), configured to perform variousdata processing operations. Compute device 105 also includes a networkcommunication interface (not shown) to connect to PSMP server 109 vianetwork 103 and other suitable electronic components. Examples devicesrepresented by compute device 105 can include a personal computer,portable computer, smartphone, tablet, notepad, dedicated servercomputer devices, any type of communication device, and/or othersuitable compute devices. As described in further detail herein, in someinstances, compute device 105 can be connected to network 103 via anintranet, an Internet Service Provider (ISP) and the Internet, acellular network, and/or other suitable network communicationtechnology.

PSMP server 109 can include one or more computer processors and computermemories (including transitory computer memory and/or non-transitorycomputer memory), which are configured to perform various dataprocessing and communication operations associated with prediction andestimation of manufacture process of physical objects. In general, PSMPserver 109 receives and processes prediction requests and electronicfiles received from compute device 105. Further components of PSMPserver 109 are discussed with reference to FIG. 2.

In some implementations, storage device 111 can be physically integratedto PSMP server 109; in other implementations, storage device 111 can bea repository such as a Network-Attached Storage (NAS) device, an arrayof hard-disks, a storage server or other suitable repository separatefrom PSMP server 109. In some instances, storage device 111 can includetrained machine-learning models configured to predict manufactureappraisals, processes, events; such machine-learning models are trainedwith data collected over time to optimize one or more machine learningmodels after deployment. Storage device 111 can also include sets ofcomputer executable instructions to apply symbolic functions overparameters or attributes calculated from digital models, datasets withgrounded axioms generated through estimations or predictions derivedfrom digital model, and other suitable type of executable-instructionand data associated with the manufacture of a physical object.

In some instances, user 101 can access the services provided by PSMPserver 109 through compute device 105 by, for example, entering on a webbrowser a Uniform Resource Locator (URL) corresponding to a domainhosted by PSMP server 109. Accordingly, a web page with GUI 113 isdisplayed on compute device 105. User 101 can upload CAD file 106 toPSMP server 109 via network 103 with one or more prediction requestsassociated with the manufacture of a physical object represented in adigital format within CAD file 106. Upon reception of CAD file 106, PSMPserver 109 executes one or more processes depending on requestssubmitted by user 101. In some instances, PSMP server 109 can fetch orretrieve one or more data structures, executable machine-learningmodels, and other suitable data and/or executable instructions from aknowledge base stored in storage device 111. Accordingly, PSMP server109 processes the prediction request(s) and sends prediction report 107to compute device 105 via network 103 in near real-time.

FIG. 2 is a schematic block diagram illustrating a predictive storagedevice, a mesh analysis engine, a point cloud analysis engine, asymbolic function engine, a predictive engine, memories, and a networkcommunication interface included in PSMP server 109 according to anembodiment. In some implementations, PSMP server 109 can be a computedevice, such as a hardware platform server configured to support mobileapplications, Software as a Service (SaaS) applications, a web site,and/or other suitable applications accessible to client compute device105 in FIG. 1.

The bus 219 collectively represents system, peripheral, and/or chipsetbuses that communicatively connect numerous internal devices of the PSMPserver 109. For instance, bus 219 communicatively couples processor 207with read-only memory 209, system memory (RAM) 203, and storage device111. From these various memory units, processor 207 can retrieveinstructions to execute and/or data to perform processes fordiscretization, manufacturability analysis, and predictions of CADmodels. In some implementations, processor 207 can be a singleprocessor, a multi-core processor, a master-slave processor arrangementor other suitable compute processing device. In some implementations,processor 207 can be any suitable processor such as, for example, ageneral purpose processor, a field programmable gate array (FPGA), anapplication specific integrated circuit (ASIC) and/or other suitablehardware device.

PSMP server 109 can further include physical compute devices not shownin FIG. 3 (e.g., load balancer device, cluster of messaging servers andcluster of RTC servers) residing at a particular location or deployed ina cloud computing network environment. A cloud computing networkenvironment enables ubiquitous, convenient, on-demand network access toa shared pool of configurable compute resources (e.g., networks,servers, storage, applications, and services) that can be rapidlyprovisioned via, for instance, virtualization and released with minimalmanagement effort or service provider interaction, and then scaledaccordingly. A cloud model can be composed of various characteristics(e.g., on demand self-service, broad network access, resource pooling,rapid elasticity, and measured service), service models (e.g., Softwareas a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure asa Service (“IaaS”), and deployment models (e.g., private cloud,community cloud, public cloud, hybrid cloud, etc.)

In some implementations, PSMP server 109 can include any combination ofone or more computer-usable or computer-readable media. For example,PSMP server 109 can contain computer-readable medium including systemmemory or random access memory (RAM) device 203, a read-only memory(ROM) device 209, a magnetic storage device 111 and other suitablememory devices. In some implementations, PSMP server 109 can include ageneral purpose processor and/or on one or more specialized processorsconfigured to execute and optimize the processing tasks performed bymesh analysis engine 211, point cloud analysis engine 213, symbolicfunction 215, predictive engine 215, and other processes executed byprocessor 207.

Mesh analysis engine 211, point cloud analysis engine 213, symbolicfunction engine 215 and predictive engine 217 can be implemented ashardware, software and/or a combination of hardware and software. Forexample, storage device 111 (or other suitable memory coupled toprocessor 207 or PSMP server 109) can include processor-executableinstructions and/or data to configure processor 207 to execute the tasksperformed by engines 211, 213, 215 and 217. Accordingly, in someinstances, processor 207 can retrieve processor executable instructionsand data to execute various PSMP processes from one or more of thememory units shown in FIG. 2.

Mesh analysis engine 211 executes discretization processes of digitalmodels and computations of geometrical and physical properties of aphysical object represented by the digital model. For example, meshanalysis engine 211 can compute and/or process data corresponding to adiscretized version a digital model. Such a discretized version includesexplicit surface representations of a digital model defined as acontinuous piecewise linear surface composed by simplicial elements, forinstance, triangles, polygons, and other suitable simplicial elements.Mesh analysis engine 211 includes computer executable instructions tocalculate geometric and physical properties associated with a digitalmodel including but not limited to, Euler characteristics (i.e.,vertices, edges, and faces), oriented bounding box, center of mass,curvature estimation, symmetry, and other suitable properties. In someimplementations, mesh analysis engine 211 includes processor executableinstructions for discretization of digital models having arbitraryshapes and processor executable instructions to determine attributesfrom such discretized representations of digital models. Further detailswith respect to mesh analysis engine 211 are discussed with reference toFIG. 3.

Point cloud analysis engine 213 computes a three dimensional (3D) pointcloud processes of a digital model and calculates geometrical andphysical properties of a physical object derived from such a pointcloud. For example, point cloud analysis engine 213 can instantiate andprocess a data structure with a set of 3D coordinates representing thedigital model (i.e., point cloud). Some data structures to define pointclouds include vectors of values with unique indexes corresponding toeach point, octrees, and other suitable data structures. Such a set of3D coordinates defines a point cloud corresponding to the digital model.

Point cloud analysis engine 213 executes one or more processes tocalculate implicit or volumetric properties from a point cloud. Suchprocesses include but are not limited to processes based on functions todetermine continuous algebraic surfaces, radial basis processes,functions to define discrete voxelizations of a digital model (i.e.,discretization of a 3D digital model into discrete elements of volumethat constitute a 3D space corresponding to a physical object), andother suitable implicit and volumetric functions. In some furtherimplementations, point cloud analysis engine 213 includes computerexecutable instructions to calculate geometric and physical propertiesassociated with a digital model including but not limited to, symmetricand asymmetric properties of a physical object represented in thedigital model, distances between surfaces of a physical objectrepresented in the digital model, and other suitable properties. In somefurther implementations, cloud point analysis engine 213 includesprocessor executable instructions for defining implicit or volumetricrepresentations of 3D models having arbitrary geometric shapes andprocessor executable instructions for determining attributes from suchimplicit or volumetric representations. Further details with respect topoint cloud analysis engine 213 are discussed with reference to FIG. 3.

Symbolic function engine 215 processes a set of symbolic functionsdetermined based on evolutionary computation models trained with samplesincluding a representative collection of digital models of a domain ofmechanical parts, products, or other suitable physical objects. Such aset of symbolic functions can be stored in a corresponding set of datastructures and/or processor executable instructions in a local memory orother memory operationally coupled to symbolic function engine 215.Accordingly, symbolic function engine 215 can determine functional formsand/or approximations of targeted functions, describing physical and/orgeometric attributes of a physical object represented in a digitalmodel. For example, symbolic function engine 215 can determinepolynomial terms, cross terms, transcendental functions, and othersuitable symbolic function elements to identify shapes of a digitalmodel.

In some instances, when a matching error rate between, for example, meshscheme attributes and a set of symbolic functions retrieved from memoryreaches a predetermined threshold, symbolic function engine 215 canexecute one or more processes to retrain evolutionary computation modelssuch that the matching error rate is decreased to an acceptable level.Accordingly, symbolic generator engine 215 can adapt or learn while inproduction after analyzing values of datasets that are substantiallydifferent from values of datasets included used as training sets of theevolutionary model. In some instances, complex physical attributes canbe generated based directly on data provided in a digital model, or datagenerated by another engine (for example mesh analysis engine 211 and/orpoint cloud analysis engine 213), via the symbolic function modelsincluded in symbolic function engine 215. Further details with respectto symbolic function 215 are discussed with reference to FIG. 3 and FIG.9.

Predictive engine 217 includes a set of trained machine-learning modelsand other suitable computation models to infer axioms regarding aphysical object represented in a digital model. Predictive engine 217can take as input one or more attributes included in a digital model orattributes or parameters generated by the mesh analysis machine engine211, point cloud analysis engine 213, and/or symbolic function 215.Thus, in some instances, predictive engine 217 can infer sufficientaxioms to determine predictions by processing outputs generated by oneor more of mesh analysis engine 211, point cloud engine 213, symbolicfunction 215, and/or data included in the electronic file with thedigital object. Predictive engine 217 also includes a knowledgeaggregator and reasoning engine (not shown in FIG. 2) that processinferred axioms to determine predictions associated with the manufactureprocess of a physical object represented in a digital model. Furtherdetails of predictive engine 217 are discussed with reference to FIG. 6.

Bus 219 can also couple PSMP server 109 to network 103 shown in FIG. 1through a network communication interface 205. Network communicationinterface 205 can include one or more wireless transceivers forperforming wireless communication and/or one or more communication portsfor performing wired communication. In some implementations, networkcommunication interface 205 can be configured to transmit digitalprediction reports in response to requests of manufacture processesprediction reports sent by remote compute devices. In this manner, PSMPserver 109 can be part of a network of computers (for example a localarea network (“LAN”), a wide area network (“WAN”), or an Intranet, or anetwork of networks, for example the Internet.

In some implementations, PSMP server 109 includes an output deviceinterface not shown in FIG. 2, for example, printers, audio devices(e.g., speakers), haptic output devices, and display devices (e.g.,cathode ray tubes (CRT), liquid crystal displays (LCD), gas plasmadisplays, touch screen monitors, capacitive touchscreen and/or othersuitable display device). Some implementations include devices thatfunction as both input and output devices (e.g., a touchscreen display).Accordingly, a user can submit prediction requests directly to PSMPserver 109 with no need of network 103.

FIG. 3 is a flowchart illustrating a method to generate a set ofpredictions associated with manufacture processes of a physical object,according to an embodiment. PSMP server 109 can receive a CAD file orother suitable electronic file with a design of a physical model at 301.In some instances, a CAD file or other suitable electronic file caninclude data points corresponding to a mesh scheme of the digital model.Thus, PSMP server can extract the mesh scheme data points from theelectronic file. In some other instances, when preprocessed dataspecifying such a mesh scheme is not included in the electronic file,PSMP server 109 can execute one or more processes to generate a meshscheme of the digital model at 303 as discussed above with reference tomesh analysis engine 211 shown in FIG. 2.

In some instances, a mesh scheme can include data structures containinginformation related to share vertices in a table of vertices. Such datastructure encode simplicial elements as sets of n-tuples, for example,triangles encoded as triples of indices into the table of vertices. Amesh scheme can further include one or more processes for local andglobal traversal of a mesh including: 1) processes to access individualvertices, edges, and faces; 2) processes to determine a next edge in aface, also known as degree of a face and inverse operation for aprevious half-edge; 3) processes to access faces attached to an edge; 4)processes to determine starting and/or ending vertices of an edge; andother suitable processes.

Some specific data structures to implement a mesh scheme includedirected-edge, winged-edge, quad-edge, half-edge, and other suitabledata structures. These data structures are example data structures thatcan be used interchangeably to define mesh schemes; thus, they are notintended to suggest any limitation as to the scope of use and/orfunctionality of the presently disclosed subject matter. Further detailsregarding examples of half-edge data structures and directed-edge datastructures are discussed below.

A half-edge data structure can include a set of half-edge structures.Each half-edge structure contains data values corresponding to a vertexpointed by the half-edge, adjacent faces, pointer to a next half-edge,pointer to an inverse or opposite half-edge, previous half-edge, andother suitable data fields and substructures. In some instances, theimplementation of such half-edge structures can be implemented by usingpointers, and/or indices stored in arrays for an efficient memoryallocation.

A directed-edge data structure can include indices as references to meshelements e.g., vertices, faces, half-edges, and other suitable elements.Indices of directed-edge structures are defined by a set of rules suchthat the structures implicitly encode connectivity information ofsimplicial elements of a mesh. To be more specific, indicescorresponding to half-edges of a triangular face can be given by:

halfedge(f,i)=3f+1, i=0,1,2  (1)

where f is the index corresponding to a triangular face. Then, an indexof an adjacent face and its index within that face are given by:

face(h)=h/3  (2)

where h is an index of a half-edge.

An instance of a mesh is analyzed at 305 to determine physical and/orgeometric attributes of the physical model represented in an electronicfile. Example of attributes calculated from a mesh scheme include a datavalue corresponding to a volume of the physical object, data valuescorresponding to a surface areas of the physical object, 3-tuple datastructures including a length value, a width value, and a height valueassociated with a prism or bounding box enclosing the digital model,3-tuple data structures with coordinate data values of a center of theprism, data values corresponding to volume of convex hulls enclosingdigital models, data values corresponding to moments of inertia ofphysical objects, data values corresponding to surface areas of aphysical object associated with a computer numerical control machineoperation, data values corresponding to non-planar surface areasassociated with the physical object, data values corresponding to thenumber of manufacture tools directions associated with a physicalobject, data values corresponding to a number of holes associated with aphysical object, a data value corresponding to a determination ofwhether or not a digital model specifies a well-defined shape, and othersuitable attributes.

A point cloud scheme is generated at 307 defined by data points in athree-dimensional coordinate system. Such a point cloud scheme isgenerated for example by point cloud analysis engine 213 discussed withreference to FIG. 2. In general, a point cloud scheme defines a digitalmodel as a collection of 3D coordinates. Point cloud analysis engine 213can determined attributes of a physical object based on implicit orvolumetric properties determined from a point cloud generated from dataincluded in a digital model, or data generated by another engine, forexample, mesh analysis engine 211.

In some implementations, a point cloud scheme generated at 307 canclassify each 3D point of an embedding space of solid objects of adigital model. Each 3D point can be flagged with a value indicatingwhether the point lays either inside, outside, or exactly on the surfacebounding a solid object. Generation of point cloud scheme can includeprocesses for downsampling, denosing, merging, estimation of normal, andother suitable point cloud processing functionalities.

In some implementations, at 309, one or more 3D shape distributionscontained within a point cloud scheme can be determined. A shapedistribution can be defined as a unique or signature shape sampled froma shape function. In some implementations, shape distributions can bedefined as a function of distance(s) between two random points laying ona surface of a digital model. More specifically, a shape distributioncan be defined as a set of histograms of distances between randomsamples of point cloud points, broken down by Euclidean distance andnormal distance, full histograms, interior distances, exteriordistances, and any combination of distances thereof.

A point cloud scheme is partitioned into a set of shape distributions at309. Accordingly, geometric, physical and other suitable properties orattributes can be calculated from functions defining such a set of shapedistributions. Advantageously, computational load is minimized bycomparing probabilities of distributions, instead of alternative shapematching methods requiring pose registration, feature correspondence,model fitting or other expensive functions. In some instances,dissimilarities between sampled distributions of shape functions areused to discriminate between different physical object represented in aphysical model.

At 309, based on the point cloud scheme, an axial symmetry of a convexhull enclosing the digital model, a data value associated with an axialsymmetry of a shape defined based on a difference between the convexhull and the digital object and other suitable geometric or physicalattributes of the physical object represented in the digital model, aredetermined.

In some implementations, the mesh scheme generated at 303, mesh analysisperformed at 305, the point cloud scheme generated at 307, point cloudanalysis performed at 309, and other suitable data structures andprocesses can be implemented using one or more commercial or public meshand point cloud processing libraries including, for example,Computational Geometric Algorithms Library™ (CGAL), OpenMesh™, WolframMathematica™, MathWorks Matlab™, and other libraries and/or tools forgenerating and processing mesh schemes. For example Wolfram Mathematica™includes libraries to compute discretization of two dimensional andthree dimensional graphics to mesh-based regions, compute convex hull ofa given set of points, compute a distance to a nearest point in a meshand other suitable functions for discretization and analysis of meshschemes. For another example Matlab™ includes a 3D point cloudprocessing toolbox with functions to read, write, and store point cloudsand other point cloud operations and utilities.

In some implementations, a collection of shape distributions can bestored in the storage device 111 shown with reference to FIG. 1 and FIG.2 or other suitable memory repository. Accordingly, a set of candidateshape distributions can be retrieved on demand, in near real-time aspre-classifiers of shapes in a digital model and can be used as input topredictive engine 217 shown with reference to FIG. 2.

At 311, a set of axioms associated with the manufacture process of aphysical object represented in a digital model is determined. PSMPserver 109, for example, can rely on machine-learning models implementedin predictive engine 217 discussed above with reference to FIG. 2. Insome implementations, machine-learning models can take as input any ofthe outputs generated by mesh analysis engine 211, point cloud analysisengine 213, symbolic function engine 217, and/or data included in theelectronic file with the digital model. Some of the axioms associatedwith the manufacture of a physical object determined at 311 include, forexample, set-up time, cycle time, number of Computer Numerical Control(CNC) operations, whether or not a mill machine is required, whether alathe machine is required, whether a sheet metal process can be used,what type(s) of blank can be used, what type(s) of fixtures can be used,and/or other suitable axioms associated with the manufacture of aphysical object.

Set-up time refers to the time to prepare machine(s) for the manufactureof one instance of a physical object. Cycle time refers to the time ittakes to manufacture a physical object not including the set-up time.Cycle time can be measure from “start to start” i.e., the starting pointof one product's processing in a specified machine or operation untilthe start of another similar product's processing in the same machine orprocess. Cycle time can be specified in categories including: 1) ManualCycle Time: the time loading, unloading, flipping/turning parts, addingcomponents to parts while still in the same machine/process; 2) MachineCycle Time: the processing time of the machine working on a part; 3)Auto Cycle Time: the time a machine runs un-aided (automatically)without manual intervention; 4) Overall Cycle Time: the complete time ittakes to produce a single unit (this term is generally used whenspeaking of a single machine or process); 5) Total Cycle Time: thisincludes all machines, processes, and classes of cycle time throughwhich a product must pass to become a finished product and othersuitable categories. Number of Computer Numerical Control (CNC)operations refers to the number of CNC operations of one or moremanufacturing machines required in the manufacture a physical object.Type of blank(s) refers to a categorical value indicating cylindrical,rectangular or other suitable type of blanks that can be used inshearing processes for the manufacture of a physical object. Type offixture(s) refers to a categorical value indicating a type of supportdevice used during the manufacture of a physical object. Fixture typesinclude parallel jaw vice, custom soft jaw vice, custom vacuum fixture,custom bold-down fixture, and other suitable types of fixtures.

At 315, a set of predictions associated with the manufacture process ofa physical object is generated. As discussed above, such predictions canbe generated by predictive engine 217 with reference to FIG. 2. Such aset of predictions can be derived from any of the outputs generated bymesh analysis engine 211, point cloud analysis engine 215, symbolicfunction engine 217, any of the axioms determined at 311, data includedin the electronic file with the digital model and/or any combinationthereof.

FIG. 4 is a flowchart illustrating a method to generate a set ofpredictions associated with manufacture processes of a physical object,according to another embodiment. The method illustrated in FIG. 4 is insome aspects functionally similar to the method discussed with referenceto FIG. 3. For example, the operations performed at 301, 303, 305, 311,and 315 discussed with reference to FIG. 3 are executed in the methodillustrated in FIG. 4. In FIG. 4 however, PSMP server 109 can calculategeometric and/or physical attributes at 407 from a digital model basedon symbolic functions generated by evolutionary computation models inconjunction with symbolic regression.

In this instance, symbolic function engine 215 discussed above withreference to FIG. 2 receives geometric and/or physical attributescorresponding to a physical object generated at 305 and/or mesh schemegenerated at 303. Thereafter, a matching process between targetedfunctions and candidate symbolic functions is executed and a set ofexpressions in a functional form is determined. PSMP server 109 candetermine sample data points, geometrical and/or physical attributesincluding implicit attributes, volumetric attributes and other suitablephysical and geometric attributes from a digital model based on thedetermined set of expressions. PSMP server 109 then uses the attributesdetermined at 407 as inputs for machine learning models executed at 311.Further discussion of the application of evolutionary models inconjunction with symbolic regression processes are provided withreference to FIG. 7 and FIG. 8.

FIG. 5 is a schematic block diagram illustrating components of apredictive engine in a predictive system for manufacture processes,according to an embodiment. Predictive engine 217 includes independentvariables preprocessor 511; predictive machine learning models 501; datastructures for the application of prediction models, for example,trained tree data structures 503, 505, and 507; and knowledge aggregatorand reasoning engine 509.

Independent variables preprocessor 511 performs several operations overdatasets generated from mesh analysis engine 211, point cloud analysisengine 213, and/or symbolic function engine 215 (shown in FIG. 2).Independent variable preprocessor 511 determines specific parameters,forms, and/or notations used for the operation of predictive machinelearning models 501. Thus, operations performed by independent variablespreprocessor 511 include, for example, centering, scaling, parsing,performing imputations over datasets, performing descriptive orinferential statistical analysis over datasets, and/or other suitableoperations used to shape datasets to be used as inputs for predictivemachine learning models

Predictive machine learning models 501 includes a collection of trainedmachine-learning models to generate a set of axioms associated with themanufacture process of a physical object. Data structures, rules,heuristics, and knowledge bases on which these learning models rely canchange over time. Stated differently, machine learning models 501 adaptto new trends and insights identified after their deployment in aproduction environment. Thus, machine learning models 501 are notlimited to the knowledge acquired during their training phase. Someinsights from which machine learning models 501 keep learning includenew market trends, new types or models of manufacture machines, unseenshapes not included in a training set, new manufacturing shops enteringa market, emerging technologies in the manufacture field, and othersuitable insights associated with the manufacture of physical objects.

Predictive machine learning models 501 can include, for example, any ofleast squares regression models, logistic regression models, supportvector machines models, stochastic gradient descent models, nearestneighbors classification and/or regression models, Gaussian processclassification and/or regression models, neural network classificationand/or regression models, ensemble methods models, sequentialapplication models, random forest classifiers models, extremelyrandomized trees regression models, deep learning models, and/or othersuitable machine learning models.

Trained tree data structures 503, 505, and 507 are each an example ofdata structures used by predictive machine learning models 501.Specifically, in some implementations, trained tree data structuresshown in FIG. 5 can be used by classifiers and/or regressors based on,for example, random forest machine learning models, extremely randomizedtrees machine learning models, logistic regression machine learningmodels, and/or other suitable machine learning models based on tree datastructures.

In some implementations, machine learning models 501 are configured toimplement random forest classifiers. Random forest classifiers include acollection of de-correlated decision trees used to classify, forexample, types of CNC processes that can be used to manufacture aphysical object and other suitable axioms associated with manufactureprocesses. Some examples of CNC processes that can be determined throughrandom forest classifiers include milling processes, lathe processes,sheet metal processes, and other suitable manufacture processes.

Random forest classifiers can be implemented to determine fabricationtechnologies or types of machine(s) that can be used to manufacture aphysical object, for instance, additive manufacturing machines,subtractive manufacturing machines, casting manufacturing machines,injection molding manufacturing machines, hybrid machines, and othersuitable types of manufacturing machines. Examples of additivemanufacturing machines include, selective laser sintering machines,manufacturing machines based on PolyJet technology, direct metal lasersintering machines, fused deposition modelling machines, binder jettingmachines, stereolithographic machines, and other suitable additivemanufacturing machines. Examples of subtractive manufacturing machinesinclude CNC milling machines, CNC turning machines, laser cuttingmachines, waterjet cutting machines, sheet metal punching machines,sheet metal folding machines, wire electrical discharge machining (EDM)machines, and other suitable subtractive manufacturing machines.

Random forest classifiers can be implemented to determine types offixtures used for the manufacturing of a physical object. For example, arandom forest classifier can receive inputs obtained from a mesh of adigital model corresponding to the genus of the physical object,curvature measures of the physical object, aspect ratio of the physicalobject, or other suitable inputs to determine whether a parallel jawvice, custom soft jaw vice, custom vacuum fixture, custom bolt downfixture, or other suitable fixture is used for the manufacture of aphysical object.

Random forest classifiers rely on N number of de-correlated decisiontrees. For a given input, each of the N decision trees generates anaxiom. In some instances, a final classification axiom is determined bycalculating, for example, the mode of the set of axioms generated by theN decision trees, the average or other suitable descriptive statistic.Advantageously, random forest classifiers mitigate a bias-variancetradeoff characteristic of classifiers based on single decision trees.Bias is the error resulting from erroneous assumptions implied in aconfiguration of a machine learning algorithm. Variance is an errorranging from sensitivity to small fluctuations, which depends ondatasets used during the training phase of a machine learning model.Random forest algorithms mitigate the bias-variance tradeoff through abagging process in which noisy and unbiased models are averaged togenerate a model with low variance. A further discussion regarding theimplementation of predictive machine learning models based on randomforest trees is provided with references to FIG. 9 and FIG. 10.

In some implementations, machine learning models 501 are configured toimplement extremely randomized trees regressors and classifiers.Extremely randomized trees are built from a full learning sample in atop-down procedure, starting with the root node and adding, at eachstep, a new test at one of the terminal nodes of a partially developedtree. Several unpruned trees can be implemented in the extremelyrandomized trees machine learning model. In some implementations, suchtrees can be unpruned decision trees and/or regression trees. Nodes inextremely randomized trees are split according to random cut-points.Because of the randomization of cut-points, extremely randomized treescan produce classification and/or decision axioms faster than otherstandard types of tree structures. Examples of an splitting process fornumerical attributes used in extremely randomized trees machine learningmodel are provided below:

  Split a node(S) Input: local learning subset S corresponding to thenode to be split Output: a split [a < ac] or nothing - If Stop split(S)=TRUE then, return nothing. - Otherwise select K attributes {a₁, . . . ,a_(k) } among all non-constant (in S) candidate attributes; - Draw Ksplits {s₁, . . . , s_(k) }, where s_(i) = Pick a random split(S, a_(i)), ∀i = 1, . . . , K; - Return a split s_(*) such that Score(s*, S) =max_(i)=₁,...,_(k) Score(s_(i) , S). Pick a random split(S,a) Inputs: asubset S and an attribute a Output: a split - Let a^(s) _(max) and a^(S)_(min) denote the maximal and minimal value of a in S; - Draw a randomcut-point a^(c) uniformly in [a^(S) _(min), a^(S) _(max)]; - Return thesplit [a < ac]. Stop split(S) Input: a subset S Output: a boolean - If|S| < n_(min), then return TRUE; - If all attributes are constant in S,then return TRUE; - If the output is constant in S, then return TRUE; -Otherwise, return FALSE.

Where K is the number of attributes randomly selected at each node andnmin is the minimum sample size for splitting a node. The splittingprocess shown above is executed multiple times over a full learningsample to generate a machine learning model with N number of treestructures. In some instances, when a model based on extremelyrandomized trees is used to implement a classifier, axioms generated byeach of the N trees are aggregated to reach a final axiom. Such a finalaxiom is determined based on a majority vote elicited from each of the Ntrees. In other implementations, when a model based on extremelyrandomized trees is used to implement a regressor, a final axiom can bedetermined by calculating an arithmetic average of the set of axiomsgenerated by the N trees.

Examples of machine learning models 501 based on extremely randomizedtrees include regressors to determine axioms associated with themanufacture of a physical object including metrics such as setup time,cycle time, number of CNC operations; these metrics are discussed abovewith reference to FIG. 3 at 311. A full learning sample used to trainextremely randomized trees can be derived from recorded manufacturetimes and CNC operations of known physical objects. Further detailsregarding training processes of machine learning models 501 arediscussed with reference to FIG. 6.

In some further implementations, machine learning models 501 areconfigured to implement logistic regression classifiers. Logisticregression models are a type of regression models where the dependentvariables are categorical. The model of logistic regression is based ondifferent assumptions (about the relationship between dependent andindependent variables) from those of linear regression. The conditionaldistribution in a logistic regression model is a Bernoulli distribution,because the dependent variable is binary. Second, the predicted valuesare probabilities and are therefore restricted to (0,1) through thelogistic distribution function because logistic regression predicts theprobability of particular outcomes. Some of the axioms generated bylogistic regression classifiers include what type of blanks can be usedin the manufacture process of a physical object, and other suitableaxioms expressed as true or false associated with the manufacture of aphysical object. For example, given an axial symmetry of a convex hullenclosing a digital model (i.e., hull symmetry) a logistic regressionmodel can determine a categorical value corresponding to a blank typethat can be used to manufacture a physical object. In some instances,CNC processes including milling processes, lathe processes, sheet metalprocesses, and other suitable manufacture processes, can be determinedthrough logistic regression models.

The axioms generated by the machine learning models 501 can be specificto one or more of an additive manufacturing machine, subtractivemanufacturing machine, casting manufacturing machine, injection moldingmanufacturing machine, hybrid manufacturing machine and other suitablefabrication technologies.

The axioms generated by the machine learning models 501 can be specificor tailored to numerous fabrication materials including metals,plastics, and other suitable fabrication materials. Examples of metalsinclude aluminum alloys, stainless steel alloys, steel alloys,brass/bronze alloys, copper alloys, nickel alloys, titanium alloys,magnesium and other suitable metals and alloys. Examples of plasticsinclude, acrylonitrile styrene acrylate, acrylonitrile butadienestyrene, polycarbonate, Ultem™, Delrin®, Nylon, and other syntheticpolymers and plastics.

Knowledge aggregator/reasoning engine 509 receives as input one or moreaxioms generated by predictive machine learning models 501, andgenerates predictions associated with the manufacture of a physicalobject. In some instances, predictions can be based on the axiomsgenerated by two or more machine learning models by combining the axiomsusing an average function, median, or other suitable type of descriptivestatistical function. In other instances, axioms generated by differentlearning models can be evaluated through a function, such that an axiomor a set of axioms showing lower error levels and/or highest confidencevalues are selected from a set of candidate axioms. In other furtherinstances, one or more axioms generated by machine learning models 501can serve as input to a reasoning engine. Rules in the reasoning enginecan be expressed in data structures denoting symbolic logicpropositions, terms, and/or relations to support the reasoning ofpredictive engine 217. In yet some further instances, predictions can begenerated through sets of linear and non-linear functions. For example,a predicted cost associated with the manufacture of a physical objectcan be calculated by engine 509 as a function of axioms indicating anumber of CNC operations used to manufacture an object, setup time, andcycle time multiplied by machine rate (including fixed costs, operatingcosts and/or labor costs) and further multiplied by a quantity ofobjects requested to be manufactured.

Knowledge aggregator/reasoning engine 509 can determine mechanicalproperties of a physical object specified in a digital model that dependon fabrication materials. Some mechanical properties dependent onfabrication materials determined at engine 509 include mechanicalproperties associated with uniaxial mechanical responses, thermodynamicsmechanical responses, stress and strain mechanical responses, yield andplastic flow mechanical responses, fracture mechanical responses, andother suitable types of mechanical responses associated with a physicalobject to be built in a determined fabrication material. More specificexamples of such mechanical properties include elastic modulus; tensilestrength; tensile elongation; shear modulus; shear strength; flexuralstrength; flexural modulus; compression strength; compression modulus;fracture toughness; Rockwell hardness (HR) including HRA for tungstencarbides, HRB for aluminum, brass and soft steels, and HRC for hardsteels (greater than HRB 100); coefficients of thermal expansion; andother suitable properties dependent on fabrication materials. Mechanicalresponses can be simulated with libraries from custom-made simulationsoftware, open source software solutions, and/or commercial softwaresuch as AudoDesk® AutoCAD®, SolidWorks® or other suitable software.

In some implementations, knowledge aggregator/reasoning engine 509 canpredict whether is feasible or not to manufacture a physical objectusing a specific fabrication material. In some instances, when isfeasible to manufacture a physical object on more than one fabricationmaterial, engine 509 can execute a multi-objective optimization based onuser specified requirements to suggest one or more fabrication materialsand/or fabrication technologies. The suggested fabrication materials canbe expressed, for example, as an Edgeworth-Pareto solution based on userrequirements and mechanical response properties of the physical objectdependent on fabrication materials.

FIG. 6 is a flowchart illustrating a training phase method for machinelearning models of a predictive system for manufacture processes,according to an embodiment. PSMP server 109 (shown in FIG. 1) canimplement supervised, unsupervised, and/or reinforcement based machinelearning models. The method illustrated in FIG. 6 is an example ofsupervised learning method; variations of the exemplary training methoddiscussed below, however, can be implemented to train unsupervisedand/or reinforcement machine learning models. Although the method ofFIG. 6 is discussed with respect to PSMP server 109, the method can beused with other systems.

PSMP server 109 receives, at 601, a corpus of raw datasets. Each datasetin the corpus includes a labeled shape associated with an array datastructure: the shapes can be 2D shapes, 3D shapes or any other shapedefined in other suitable dimension space. The array data structurestores a set of data values corresponding to geometric and/or physicalattributes of such a shape.

PSMP server 109 generates, at 603, a training set by executing one ormore operations over the corpus received at 601. The operations,executed at 603, include the selection of a minimum set of shapeattributes that can be used by, for example, a classifier todifferentiate among different shapes. In some instances, such a minimumset of shape attributes can be determined by eliminating shapeattributes that are highly correlated and thus, redundant. Accordingly,a dimensionality reduction process can be applied to compress theattributes onto a lower dimensionality subspace and advantageously,storage space can be minimized, resulting in the improvement oroptimization of computation load used by the method. In some furtherinstances, the attributes associated with a shape are scaled, forexample, each selected attribute is expressed as a value ranging from [0. . . 1] and/or a standard normal distribution with zero mean and unitvariance.

In some implementations, one or more machine learning models are trainedat 605. The machine learning models trained at 605 are selected based ondifferent criteria depending on the problem to be solved and/or dataavailable in the training set. For example, machine learning classifierscan suffer from different degrees of bias. Accordingly, more than onemachine learning model can be trained at 605, optimized andcross-validated at 607. Thus, a subset of the trained machine learningmodels can be selected to build predictive engine 611 according to, forexample, classification accuracy, defined as the proportion of correctlyclassified instances.

In some instances, machine learning models built at 611, are furtherevaluated using an unseen test dataset 609. Thus, the predictive enginebuilt at 611 generates classification values and/or predicted values at613. Classification and/or prediction values are evaluated at 615 todetermine whether such values have achieved a desired accuracy level.When such a desired accuracy level is reached, the training phase ends;when the desired accuracy level is not reached, however, then asubsequent iteration of the process shown in FIG. 6 is performedstarting at 601 with variations such as, for example, considering alarger collection of raw data.

FIG. 7 is a flowchart illustrating an example of a method to implementan evolutionary model of a predictive system for manufacture processes,according to an embodiment. In some implementations, one or moreevolutionary algorithms can be used to determine symbolic functions tocalculate geometric and/or physical parameters associated with aphysical object. In general, evolutionary models work under a principlethat specifies that given an initial population of individuals(samples), a rise in the fitness of such a population is experiencedover time due to natural selection. When a domain of candidate solutionsis represented as an initial population, an optimal solution(s) can bedetermined over time through the execution of evolutionary models.

Several types of evolutionary models can be implemented in PSMP server109 including models based on genetic algorithms (GA), models based onevolution strategy (ES) algorithms, models based on evolutionaryprogramming (EP), models based on genetic programming (GP), and othersuitable evolutionary algorithms. Accordingly, different data structurescan be used to implement such algorithms including strings over a finitealphabet, real-valued vectors, finite state machines, tree structuresand other suitable data structures.

In some implementations, PSMP server 109 initializes, at 701, apopulation by generating a random sample of candidate solutions. Forexample, candidate solutions can be provided as set of random shapedistributions determined as a function of distance(s) between two randompoints laying on a surface of a digital model. As discussed withreference to FIG. 3, shape distributions can be defined as a function ofdistance(s) between two random points laying on a surface of a digitalmodel. Each of the candidate solutions is evaluated at 703 based on afitness function. Such a fitness function is used to assign a qualitymeasure to a candidate solution.

A termination condition is then evaluated at 705. A terminationcondition can based on any of: 1) a known optimal fitness level, 2) atime based condition, 3) a total number of executed fitness evaluations,4) a threshold representing fitness improvement shown by a number ofgenerations, 5) a measure of how much population diversity drops over anumber of generations, and/or any combination of these conditions andother suitable conditions to prevent the process of FIG. 6 fromiterating an infinite number of times while providing an optimized setof solutions upon the last executed iteration.

In some instances, when the termination condition at 705 is met, thenthe process stops. In other instances, when the termination condition at705 is not met, then a set of candidate solutions is selected as parentsbased on fitness and/or quality criteria. Selected candidate solutionsare used as parents to generate a next generation of candidatesolutions. For example, pairs of candidate solutions selected at 707 arerecombined at 709 to generate offspring candidate solutions.

A stochastic mutation operator is applied at 711 to offspring candidatesolutions. In some instances, such a mutation operator causes a random,unbiased change over an offspring. Thereafter, the fitness level of eachcandidate solution of a new generation is determined at 713. In someimplementations, the fittest individuals or candidate solutions areselected for the next generation at 715, i.e., a next iteration of theloop shown in FIG. 7 until termination condition 705 is satisfied.

FIG. 8 is a data structure for symbolic regression built via anevolutionary model of a predictive system for manufacture processes,according to an embodiment. A specific application of evolutionaryprocess discussed with reference to FIG. 7 in conjunction with symbolicregression is described below. In some implementations, evolutionarymodels are used to infer function approximations represented in treestructures as the example shown in FIG. 8. The advantage of such modelsover other function learners is that they automatically discover anunderlying structure of a function.

The objective of polynomial function approximation is to infer apolynomial p(x) that is capable of mapping reliably not only the dataprovided in a training set, but also on average unseen data, which isgenerated by sources similar to the sources used to obtained a trainingset. The approximation problem can be expressed as follow: giveninstantiated vectors of independent variables xi=(x_(i1), x_(i2), . . ., x_(id))∈R^(d) and dependent variable values y_(i)=∈R, find models p(x)optimally close to their unknown source function f(x)=y.

High-order multivariate polynomials are a universal format for functionmodeling with which any continuous mapping may be approximated up to anarbitrary precision, in average squared residual sense, if there are asufficiently large number of terms. Such polynomials are defined by thepower series:

p(x)=a ₀+Σ_(m=1) ^(M) a _(m)Γ_(j=1) ^(d) x _(j) _(jm) ^(r)  (3)

where a_(m) are the term coefficients, m ranges up to a pre-selectedmaximum number of terms M: m≤x_(j) are the independent variable valuesof the input vector x, j≤d numbers; and r_(jm) is bound by a maximumpolynomial order (degree) s:Σ_(j) ^(d)r_(jm)≤s s for every m.

Polynomials (3) are combinations of terms that are linear in thecoefficients, and non-linear in the variables. Cross-product and powerterms are derived from standard basis Ø=(1, x₁, x₂ . . . , x_(d), x₁ ²,x₁x₂, x₁x₃, . . . , x₁ ³, x₁ ²x₂, x₁ ²x₃, . . . ). Using this basis Ø, ahigh-order multivariate polynomial can be defined with the equation:

p(x)=Σ_(m=0) ^(M) a _(m) Øm(x)  (4)

where a_(m) are the coefficients, and Ø_(m) are the basis functions fromØ. Basis functions of h_(m) are used to emphasize that only a smallsubset of Ø is considered.

In some instances an overall polynomial can be defined as a hierarchicalcomposition of simple transfer polynomials whose coefficients arecalculated by least square methods. These simple transfer polynomialsare primitive functions that once composed hierarchically do not rapidlyincrease the overall polynomial degree. In FIG. 8, simple transferpolynomials are represented in the nodes 801, 803, 805, and 807. Overallpolynomials (e.g., f(x) at the top of FIG. 8) are used to determinedifferent geometric and physical attributes of a physical object.Independent variables, x3, x5, x7, x9, and x8 can correspond to datavalues associated with a physical object generated by, for example, meshanalysis engine 211, point cloud analysis engine 213 and/or retrieveddirectly from data included in a digital model. Examples of suchindependent variables include data values corresponding to a surfaceareas of the physical object, 3-tuple data structures including a lengthvalue, a width value, and a height value associated with a prismenclosing the digital model (i.e., bounding box), 3-tuple datastructures with coordinate data values of a center of the prism, datavalues corresponding to volume of convex hulls enclosing digital models,data values corresponding to moments of inertia of physical objects,data values corresponding to surface areas of a physical objectassociated with a computer numerical control machine operation, datavalues corresponding to a number of holes associated with a physicalobject, and/or other suitable attributes.

Polynomials can be represented as tree structures. Each tree containsnodes with complete bivariate second degree transfer polynomials such asp(x)=a₀+a₁x₁+a₂x₂+a₃ x₁x₂+a₄x₁ ²+a₅x₂ ². Independent variables arerepresented as terminal leaves in the example illustrated in FIG. 8.

A polynomial can be built form a tree structure using a verticaltechnique. Having a finite number N of data D={(x_(i),y₁)}_(i=1) ^(N)from independent variable vectors x_(i) and their corresponding outcomesy_(i), mean square error methods can be used for finding polynomialcoefficients a_(m). Coefficients of the transfer polynomials at eachnode are computed by, for example, the execution of ordinary leastsquare (OLS) fitting equation provided below:

a=(H ^(T) H)⁻¹ H ^(T) y  (5)

where a is (m+1)×1 column vector, H is N×(m+1) design matrix of rowvectors h(x_(i))=(h₀(x_(i)),h_(i)(x_(i)), . . . , h_(m)(x_(i))), m≤M,i=1 N, y is the N×1 output column vector, and Ha=p.

An evolutionary model as described with reference to FIG. 7 can be usedto define polynomials for the determination of different attributes of aphysical object. For example, an initial population of trees can begenerated through a ramped half-and-half method. The best trees can beprobabilistically selected as parents of a new generation. Crossover canbe applied by selecting a random node from each parent tree as thecrossover point to generate two new offspring for the next generation.Mutation can be performed by selecting a node as the mutation point anda new node or subtree is generated originating at the mutation point andreplacing the branch that was derived originally from the selectedmutation point.

In some implementation, a collection of tree structures as the one shownin FIG. 8 are executed by symbolic regression engine 217 discussed withreference to FIG. 2. Accordingly, polynomial terms, cross terms,transcendental functions and other suitable expressions and values canbe calculated by symbolic regression engine 217.

FIG. 9 is a flowchart illustrating a method to build a random forestclassifier machine learning model, according to an embodiment. Giventraining dataset TS with a shape identifier (SID) and a collection ofphysical and/or geometric attributes below {A1, A2, A3 . . . An}, TS canbe expressed as follows:

$\begin{matrix}{{TS} = \begin{bmatrix}{A\; 1} & {A\; 2} & {A\; 3\mspace{14mu} \ldots \mspace{14mu} {SID}\; 1} \\{A\; 1} & {A\; 2} & {A\; 3\mspace{14mu} \ldots \mspace{14mu} {SID}\; 2} \\{A\; 1} & {A\; 2} & {A\; 3\mspace{14mu} \ldots \mspace{14mu} {SID}\; 3}\end{bmatrix}} & (6)\end{matrix}$

A collection of trees are generated from the training set TS. For eachtree, a different subset of random attributes is taken into account todefine a random training set. For example TS₁ and TS₂ can be defined asbelow:

${TS}_{1} = \begin{bmatrix}{A\; 1} & {A\; 2} & {A\; 3\mspace{14mu} \ldots \mspace{14mu} {SID}\; 1} \\{A\; 16} & {A\; 4} & {A\; 91\mspace{14mu} \ldots \mspace{14mu} {SID}\; 2} \\{A\; 7} & {A\; 22} & {A\; 53\mspace{14mu} \ldots \mspace{14mu} {SID}\; 3}\end{bmatrix}$ ${TS}_{2} = \begin{bmatrix}{A\; 63} & {A\; 54} & {A\; 48\mspace{14mu} \ldots \mspace{14mu} {SID}\; 1} \\{A\; 9} & {A\; 12} & {A\; 79\mspace{14mu} \ldots \mspace{14mu} {SID}\; 2} \\{A\; 68} & {A\; 12} & {A\; 31\mspace{14mu} \ldots \mspace{14mu} {SID}\; 3}\end{bmatrix}$

A decision tree is generated for each of the defined random trainingsets. The collection of decision trees is known as the random forest.Differently stated, a random forest is a classifier including acollection of tree-structured classifiers {h(x, Θk), k=1, . . . } wherethe {Θk} are independent identically distributed random vectors and eachdecision tree casts a unit vote for the most popular class at input x.In other words, building a random forest comprises the task ofgenerating random vectors to grow an ensemble of decision trees andletting those decision trees vote for the most popular class. In someimplementations a final prediction can be based on the average ofpredictions from the entire forest or a majority of vote classification.

An example of a process to implement a random forest classifier isprovided below. Assume a number of decision trees to grow is t, then foreach decision tree (at 901) select a training data subset n as shown at903 from the training set TS (e.g., bagged subset of samples orbootstrap sample). The conditional statement at 905 determines whether astop condition holds at each node of a growing decision tree. Thestopping condition depends on the selected training data subset n. Someexamples of the condition evaluated at 905 include the number oftraining samples at the node, if a maximum depth is reached or othersuitable conditions.

If such condition is satisfied, the current node becomes a leaf node anda prediction error for the decision tree is calculated at 907. If thestop condition at 905 does not hold, an internal node is grown and asplit function is selected from a pool of random functions such thattraining errors from n are minimized. The selected split functioninduces a partition of the subset n into two sets, which in turn becomethe left and right children nodes of the current node where the trainingprocedure is continued. Further details regarding an exampleimplementation of a splitting function are discussed with reference toFIG. 10.

FIG. 10 is a flowchart illustrating a method for applying a splittingcriterion to a random forest classifier machine-learning model,according to an embodiment. In some implementations, an impurity-basedGini index is used as an attribute selection measure to assess splittingcriterion. Accordingly, for a node, a subset of variables is randomlyselected at 1001. An iterative loop starts at 1003 in which for each ofthe sampled data 1005 is sorted by the chosen variable at 1007.Thereafter, at 1009, the Gini index or other suitable function iscomputed at each split point. The Gini index measures the impurity of adataset. The Gini index considers a binary split for each attribute.Gini index point of a set is defined as follows:

Gini(S)=1−Σ_(i=0) ^(m) p _(i) ²

where p_(i) is the proportion of observations or samples with a targetvariable (e.g., SID) and m is the number of different values taken bythe target variable.

FIG. 11 is a cross-entity flowchart illustrating a method to computepredictions in a predictive system for manufacture processes, accordingto an embodiment. In some implementations, compute device 103 sends anelectronic file with a digital design model (e.g., CAD) of a physicalobject at 1101 to PSMP server 109. In some implementations, meshanalysis engine 211 receives the electronic file and executes a meshanalysis on the CAD model. In some instances, the analysis executed bymesh analysis engine 211 includes a discretization (at 1103) of a CADdesign into, for example, tetrahedral, triangular, beam and/or trusselements. Thereafter, mesh analysis engine 211 can extract and/orcalculate at 1105 physical and/or geometric parameters from thegenerated mesh. Specific examples of physical and/or geometricparameters calculated by mesh analysis engine 211 were discussed abovewith reference to step 305 in the process shown in FIG. 3.

In some implementations, point cloud analysis engine 213 generates apoint cloud model at 1107 including a collection of 3D coordinatesderived from the CAD model design. Such a point cloud model is used at1109 by point cloud analysis engine 213 to determine geometric shapes,physical and/or geometric parameters. Specific examples of physicaland/or geometric parameters calculated by point cloud analysis engine213 were discussed above with reference to step 309 in the process shownin FIG. 3. Further examples of such parameters include variables basedon distance between surfaces. For instance, the proportion of a surfacethat bounds a solid region of a digital model with a thickness under agiven cutoff and/or the proportion of a surface that bounds a voidregion with a thickness under a given cutoff.

Physical and/or geometric parameters calculated by mesh analysis engine211 and point cloud analysis engine 213 are sent to predictive engine217. In some implementations, predictive engine 217 executes at 1111 oneor more machine learning models to generate axioms associated withproperties of a physical object represented in the CAD model discussedat 1101. Some examples of the predictive machines learning models usedby predictive engine 217 include regression process engines, clusteringanalysis engines, dimensionality reduction process engines, and othersuitable machine learning models discussed above with reference topredictive machine learning models 501 in FIG. 5.

In some implementations, predictive engine 217 derives inferences, at1113, based on axioms generated by machine learning models at 1111. Suchinferences include manufacturing methods, number of operations used tomanufacture a design, machine setup time, machine cycle time, stockclassification and other suitable axioms associated with the manufactureof a physical object. Thereafter, predictive engine 217 generates a setof predictions associated with the overall production process of aphysical object corresponding to the model design discussed at 1101.Such a set of CAD design production predictions is sent to computedevice 103 in near real-time at 1117. An example of manufacturepredictions displayed on a GUI is discussed below with reference to FIG.13.

FIG. 12 is an example of a signal flow illustrating a method to computepredictions associated with manufacture of physical objects based onknowledge acquisition from a random forest classifier, an extremelyrandomized trees regressor, and a logistic regression classifier,according to an embodiment. Aspects of mesh analysis engine 211 andsymbolic function engine 215 are discussed above with reference to FIG.2. Aspects of independent variables preprocessor 511 are discussed abovewith reference to FIG. 5. Random forest classifier 501B, extremelyrandomized trees regressor 501B, and logistic regression classifier 501Care examples of predictive machine learning models 501 also discussedabove with reference to FIG. 5.

In some implementations, mesh analysis engine 211 computes one or moreoperations over a discretized version of a digital model to generate, at1201, a set of physical and/or geometric parameters or attributes. Insome instances, the set of parameters generated by mesh engine 211 arepropagated, at 1215, to one or more of the symbolic function engine 215,independent variables preprocessor 511, random forest classifier 501A,extremely randomized trees regressor 501B, and logistic regressorclassifier 501C. Examples of the physical and/or geometric parameters orattributed generated by mesh analysis engine 211 include first set ofparameters includes any of a data value corresponding to a volume of thephysical object, a data value corresponding to a surface area of thephysical object, a 3-tuple data structure including a length value, awidth value, and a height value associated with a prism enclosing thedigital model, a 3-tuple data structure with coordinate data values of acenter of the prism, a data value corresponding to a volume of a convexhull enclosing the digital model, a data value corresponding to a momentof inertia of the physical object, a data value corresponding to asurface area of the physical object associated with a computer numericalcontrol machine operation, a data value corresponding to a non-planarsurface area associated with the physical object, a data valuecorresponding to a number of manufacture tool directions associated withphysical object, and/or a data value corresponding to a number of holesassociated with the physical object.

Symbolic function engine 215 receives as inputs parameters generated bymesh engine 211. Symbolic function engine 215 executes one or moreprocess according to symbolic functions determined by evolutionaryalgorithms models discussed with reference to FIG. 7 and FIG. 8.Accordingly, further physical and/or geometric parameters are generated,at 1203. For example, symbolic function engine 215 can determineimplicit or volumetric parameters associated with a digital model.Parameters generated by symbolic function engine 215 are propagated at1217 to one or more of independent variable preprocessor 511, randomforest classifier 501A, extremely randomized trees regressor 501B, andlogistic regressor classifier 501C.

Independent variables preprocessor 511 receives parameters or attributesfrom mesh analysis engine 211 and/or symbolic function engine 215.Thereafter, independent variables preprocessor 511 perform one or moreoperations, at 1205, over the received parameters so they can be used asinputs by random forest classifiers 501A, extremely randomized treesregressor 501B, and logistic regression classifier 501C. Some exemplaryoperations performed by independent variables preprocessor includescaling, for example, each received attribute or parameter to a valueranging from [0 . . . 1], a standard normal distribution with zero meanand unit variance, and other suitable forms. Such normalized or scaledversions of the parameters are then propagated, at 1219, to randomforest classifier 501A, extremely randomized trees regressor 501B andlogistic regression classifier 501C.

Random forest classifier 501A receives in some instances parameters orattributes generated from mesh analysis engine 211, symbolic functionengine 215 and/or independent variables preprocessor 511. Aspects ofrandom forest classifier are discussed with reference to FIG. 9 and FIG.10. Some of the axioms associated with the manufacture of a physicalobject determined at 1207 include whether or not a mill machine is to beused, whether a lathe machine is to be used, whether a sheet metalprocess can be used and other suitable axioms associated with themanufacture of a physical object.

Extremely randomized trees regressor 501B receives in some instances,parameters or attributes generated from mesh analysis engine 211,symbolic function engine 215 and/or independent variables preprocessor511. Aspects of random forest classifier are discussed with reference toFIG. 5. Some of the axioms associated with the manufacture of a physicalobject determined at 1207 include setup, time cycle, number of ComputerNumerical Control (CNC) operations, and/or other suitable axiomsassociated with the manufacture of a physical object.

Logistic regression classifier 511 receives, in some instances,parameters or attributes generated from any of mesh analysis engine 211,symbolic function engine 215 and/or independent variables preprocessor511. Aspects of logistic regression classifier 511 are discussed withreference to FIG. 5. Some of the axioms associated with the manufactureof a physical object determined at 1211 include what type(s) of blankcan be used, and other suitable axioms associated with the manufactureof a physical object.

Knowledge aggregator and reasoning engine 513 receives, in someinstances, axioms generated by random forest classifier 501A, extremelyrandomized trees regressor 501B, logistic regression classifier 501Cand/or other suitable machine learning models. Aspects of knowledgeaggregator and reasoning are discussed with reference to FIG. 5.Knowledge aggregator and reasoning engine generates at 1213 a set ofpredictions for the manufacture of a physical object corresponding tothe digital design model. Such predictions can be sent to a computedevice of a user requesting an assessment associated with themanufacture of a physical object.

FIG. 13 is an example of an output from a graphical user interface ofthe predictive system for manufacture processes and related to predictedoutcomes associated with the manufacture of a physical object, accordingto an embodiment. Graphical user interface 113 corresponds to thegraphical user interface shown in FIG. 1. The graphical user interface113 displays predictions generated by the PSMP server 109. Predictionsinclude a total cost 1301 to manufacture on or more physical object. Abutton 1303 to allow a user to select among different materials that canbe used to manufacture a physical object. Estimated time to receive aprototype in a variety of production grade thermoplastics, and itsassociated costs shown at 1305. A dropdown menu with availableprototyping technologies 1307 that can be used to manufacture a physicalobject specified in a digital model 1309, in this case, fuse depositionmodeling.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Where methods and/or schematics described above indicatecertain events and/or flow patterns occurring in certain order, theordering of certain events and/or flow patterns may be modified. Whilethe embodiments have been particularly shown and described, it will beunderstood that various changes in form and details may be made.Additionally, certain of the steps may be performed concurrently in aparallel process when possible, as well as performed sequentially asdescribed above. Although various embodiments have been described ashaving particular features and/or combinations of components, otherembodiments are possible having any combination or sub-combination ofany features and/or components from any of the embodiments describedherein. Furthermore, although various embodiments are described ashaving a particular entity associated with a particular compute device,in other embodiments different entities can be associated with otherand/or different compute devices.

It is intended that the systems and methods described herein can beperformed by software (stored in memory and/or executed on hardware),hardware, or a combination thereof. Hardware modules may include, forexample, a general-purpose processor, a field programmable gates array(FPGA), and/or an application specific integrated circuit (ASIC).Software modules (executed on hardware) can be expressed in a variety ofsoftware languages (e.g., computer code), including Unix utilities, C,C++, Java™ JavaScript, Ruby, SQL, SAS®, Python, Fortran, the Rprogramming language/software environment, Visual Basic™, and otherobject-oriented, procedural, or other programming language anddevelopment tools. Examples of computer code include, but are notlimited to, micro-code or micro-instructions, machine instructions, suchas produced by a compiler, code used to produce a web service, and filescontaining higher-level instructions that are executed by a computerusing an interpreter. Additional examples of computer code include, butare not limited to, control signals, encrypted code, and compressedcode. Each of the devices described herein can include one or moreprocessors as described above.

Some embodiments described herein relate to devices with anon-transitory computer-readable medium (also can be referred to as anon-transitory processor-readable medium or memory) having instructionsor computer code thereon for performing various computer-implementedoperations. The computer-readable medium (or processor-readable medium)is non-transitory in the sense that it does not include transitorypropagating signals per se (e.g., a propagating electromagnetic wavecarrying information on a transmission medium such as space or a cable).The media and computer code (also can be referred to as code) may bethose designed and constructed for the specific purpose or purposes.Examples of non-transitory computer-readable media include, but are notlimited to: magnetic storage media such as hard disks, floppy disks, andmagnetic tape; optical storage media such as Compact Disc/Digital VideoDiscs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), andholographic devices; magneto-optical storage media such as opticaldisks; carrier wave signal processing modules; and hardware devices thatare specially configured to store and execute program code, such asApplication-Specific Integrated Circuits (ASICs), Programmable LogicDevices (PLDs), Read-Only Memory (ROM) and Random-Access Memory (RAM)devices. Other embodiments described herein relate to a computer programproduct, which can include, for example, the instructions and/orcomputer code discussed herein.

What is claimed:
 1. A system for predictions of manufacturing processes,comprising: a graphical user interface tool including computerizedinstructions for receiving, at a compute device, an electronic fileincluding a digital model representative of a physical object to bemanufactured; an object analysis engine including computerizedinstructions for generating, from the digital model, a first set ofparameters associated with the physical object; a first machine learningengine including computerized instructions for generating a second setof parameters associated with the physical object from at least thefirst set of parameters; and a second machine learning engine includingcomputerized instructions for producing at least one predictive outcomeassociated with manufacturing the physical object, based at least inpart upon the second set of parameters, and based at least in part uponone or more neural network machine learning models; wherein at least oneof the one or more neural network machine learning models is aregression model; wherein the at least one predictive outcomecorresponds to a manufacturing process; and wherein the graphical userinterface tool further includes computerized instructions for providingto a user responsive information associated with manufacturing thephysical object, the responsive information being based upon the atleast one predictive outcome.
 2. The system of claim 1, wherein thefirst set of parameters include at least one of the following: (i)values corresponding to non-planar surface areas associated with thephysical object; (ii) values corresponding to a volume of the physicalobject, (iii) values corresponding to a surface area of the physicalobject, (iv) values corresponding to convex hull volume of the physicalobject, and (v) values corresponding to length, width and height of aprism enclosing the physical object.
 3. The system of claim 1, whereinat least one of the neural network machine learning models is trainedusing unsupervised machine learning.
 4. The system of claim 1, whereinthe object analysis engine comprises a mesh analysis engine.
 5. Thesystem of claim 1, wherein the object analysis engine comprises a pointcloud analysis engine.
 6. The system of claim 1, wherein the objectanalysis engine comprises a mesh analysis engine and a point cloudanalysis engine.
 7. The system of claim 1, wherein the first machinelearning engine includes a symbolic function engine includinginstructions for determining, from at least the first set of parameters,at least one of functional forms or approximations of targeted functionsdescribing at least one of physical attributes or geometric attributesof a physical object.
 8. The system of claim 7, wherein the symbolicfunction engine utilizes an evolutionary computation model trained withsamples including a representative collection of digital models ofphysical objects.
 9. The system of claim 1, wherein the second machinelearning engine produces the at least one predictive outcome basedfurther upon one or more candidate shape distributions retrieved as apre-classifier of physical object shape.
 10. The system of claim 1,wherein the second machine learning engine produces a plurality ofpredictive outcomes taken from a group consisting of: price, set-uptime, cycle time, a number of Computer Numerical Control (CNC)operations, requirement of a mill machine, requirement of a lathemachine, requirement of a sheet metal process, a type of blank used, ora type of fixture used.
 11. The system of claim 1, wherein the systemprovides the responsive information to the graphical user interface toolin near real-time upon receiving the electronic file from the graphicaluser interface tool.
 12. A system for predictions of manufacturingprocesses, comprising: a graphical user interface tool includingcomputerized instructions for receiving, at a compute device, anelectronic file including a digital model representative of a physicalobject to be manufactured, and for returning responsive informationassociated with manufacturing the physical object, the responsiveinformation including information related at least to a predictiveoutcome generated by a predictive engine; wherein the predictive outcomegenerated by the predictive engine is generated by the predictive enginebased at least in part upon (a) at least one of a set of parametersassociated with the digital model or information derived from the set ofparameters, and (b) one or more neural network machine learning models,at least one of the one or more neural network machine learning modelsbeing trained using a collection of digital models of a domain ofmechanical objects; wherein the at least one predictive outcomecorresponds to one or more of the following: predicted cost, set-uptime, cycle time, a number of Computer Numerical Control (CNC)operations, requirement of a mill machine, requirement of a lathemachine, requirement of a sheet metal process, a type of blank used, ora type of fixture used; wherein the graphical user interface alsoreceives an identification of a fabrication material; and at least oneof the one or more neural network machine learning models predictswhether or not it is feasible to manufacture the physical object usingthe identified fabrication material.
 13. The system of claim 12, whereinthe set of parameters is generated from the digital model by an objectanalysis engine based upon at least one of (i) a discretized version ofthe digital model or (ii) a point cloud processing of the digital model.14. The system of claim 12, wherein the set of parameters include (i)values corresponding to surface orientations associated with thephysical object and (ii) values corresponding to non-planar surfaceareas associated with the physical object.
 15. The system of claim 14,wherein the set of parameters further include a plurality of thefollowing: (iii) values corresponding to a volume of the physicalobject, (iv) values corresponding to a surface area of the physicalobject, (v) values corresponding to convex hull volume of the physicalobject, and (vi) values corresponding to length, width and height of aprism enclosing the physical object.
 16. The system of claim 12, whereinthe predictive engine produces a plurality of predictive outcomes takenfrom a group consisting of: set-up time, cycle time, a number ofComputer Numerical Control (CNC) operations, requirement of a millmachine, requirement of a lathe machine, requirement of a sheet metalprocess, a type of blank used, or a type of fixture used.
 17. The systemof claim 12, wherein at least one of the one or more neural networkmachine learning models is trained using unsupervised machine learning.